Generation of optimal persistent formations for heterogeneous multi-agent systems with a leader constraint

doi:10.1088/1674-1056/27/2/028901

Abstract In this study, we consider the generation of optimal persistent formations for heterogeneous multi-agent systems, with the leader constraint that only specific agents can act as leaders. We analyze three modes to control the optimal persistent formations in two-dimensional space, thereby establishing a model for their constrained generation. Then, we propose an algorithm for generating the optimal persistent formation for heterogeneous multi-agent systems with a leader constraint (LC-HMAS-OPFGA), which is the exact solution algorithm of the model, and we theoretically prove its validity. This algorithm includes two kernel sub-algorithms, which are optimal persistent graph generating algorithm based on a minimum cost arborescence and the shortest path (MCA-SP-OPGGA), and the optimal persistent graph adjusting algorithm based on the shortest path (SP-OPGAA). Under a given agent formation shape and leader constraint, LC-HMAS-OPFGA first generates the network topology and its optimal rigid graph corresponding to this formation shape. Then, LC-HMAS-OPFGA uses MCA-SP-OPGGA to direct the optimal rigid graph to generate the optimal persistent graph. Finally, LC-HMAS-OPFGA uses SP-OPGAA to adjust the optimal persistent graph until it satisfies the leader constraint. We also demonstrate the algorithm, LC-HMAS-OPFGA, with an example and verify its effectiveness.

Keywords: leader constraint heterogeneous multi-agent system optimal persistent formation minimum cost arborescence

Received: 18 July 2017
Revised: 09 October 2017
Accepted manuscript online:

PACS:	89.20.Ff	(Computer science and technology)
	87.85.St	(Robotics)
	89.65.Ef	(Social organizations; anthropology ?)
	02.30.Em	(Potential theory)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 71671059, 71401048, 71521001, 71690230, 71690235, and 71472058) and the Anhui Provincial Natural Science Foundation, China (Grant No. 1508085MG140).

Corresponding Authors: He Luo
E-mail: luohe@hfut.edu.cn

About author: 89.20.Ff; 87.85.St; 89.65.Ef; 02.30.Em

Cite this article:

Guo-Qiang Wang(王国强), He Luo(罗贺), Xiao-Xuan Hu(胡笑旋) Generation of optimal persistent formations for heterogeneous multi-agent systems with a leader constraint 2018 Chin. Phys. B 27 028901

[1]	Barrientos A G, Lopez J L, Espinoza E S, Hoyo J and Valencia G 2016 IEEE Latin America Transactions 14 1184
[2]	Guo H, Chen S, Bao A M, Behrangi A, Hong Y, Ndayisaba F, Hu J J and Stepanian P M 2016 Atmospheric Research 176 121
[3]	Nigam N, Bieniawski S, Kroo I and Vian J 2012 IEEE T Control Syst. T 20 1236
[4]	Cao X, Zhu D Q and Yang S X 2016 IEEE Transactions on Neural Networks and Learning Systems 27 2364
[5]	Giulietti F, Pollini L and Innocenti M 2000 IEEE Control Systems 20 34
[6]	Pollini L, Giulietti F and Innocenti M 2002 American Control Conference, May 8-10, 2002, Hilton Anchorage and Egan Convention Center, Anchorage, Alaska, USA, p. 2860
[7]	Ren W 2007 IET Control Theory & Applications 1 505
[8]	Yang H, Jiang B and Zhang Y M 2014 International Journal of Control Automation and Systems 12 29
[9]	Hendrickx J M, Fidan B, Changbin Y, Anderson B D O and Blondel V D 2008 IEEE T Automat. Control 53 968
[10]	Yu C and Anderson B D O 2009 Int. J. Robust Nonlin. 19 1427
[11]	Motevallian S A, Yu C and Anderson B D O 2015 Int. J. Robust Nonlin. 25 1654
[12]	Tousi M M and Khorasani K 2012 Automatica 48 410
[13]	Tousi M M and Khorasani K 2015 Int. J. Control 88 90
[14]	Hendrickx J M, Fidan B, Yu C, Anderson B D O and Blondel V D 2006 the 17th International Symposium on Mathematical Theory of Networks and Systems (MTNS2006), July 24-28, Kyoto, Japan, p. 859
[15]	Smith B S, Egerstedt M and Howard A 2009 Mobile Networks & Applications 14 322
[16]	Luo X Y, Shao S K, Zhang Y Y, Li S B, Guan X P and Liu Z X 2014 Chin. Phys. B 23 028901
[17]	Luo X Y, Li S B and Guan X P 2009 Chin. Phys. B 18 3104
[18]	Luo X Y, Shao S K, Guan X P and Zhao Y J 2013 Acta Automatica Sinica 39 1431(in Chinese)
[19]	Luo X Y, Yang F, Li S B and Guan X P 2014 Acta Automatica Sinica 40 1311(in Chinese)
[20]	Summers T H, Yu C B, Dasgupta S and Anderson B D O 2011 IEEE T. Automat. Control 56 2772
[21]	Gabow H N, Galil Z, Spencer T and Tarjan R E 1986 Combinatorica 6 109
[22]	Dijkstra E W 1959 Numerische Mathematics 1 269
[23]	Ren R, Zhang Y Y, Luo X Y and Li S B 2010 International Journal of Automation and Computing 7 557

[1]	Consensus of heterogeneous multi-agent systems based on sampled data with a small sampling delay Wang Na (王娜), Wu Zhi-Hai (吴治海), Peng Li (彭力). Chin. Phys. B, 2014, 23(10): 108901.
[2]	Finite-time consensus of heterogeneous multi-agent systems Zhu Ya-Kun (朱亚锟), Guan Xin-Ping (关新平), Luo Xiao-Yuan (罗小元). Chin. Phys. B, 2013, 22(3): 038901.
[3]	Consensus protocol for heterogeneous multi-agent systems: A Markov chain approach Zhu Shan-Ying (朱善迎), Chen Cai-Lian (陈彩莲), Guan Xin-Ping (关新平). Chin. Phys. B, 2013, 22(1): 018901.
[4]	Consensus pursuit of heterogeneous multi-agent systems under a directed acyclic graph Yan Jing(闫敬), Guan Xin-Ping(关新平), and Luo Xiao-Yuan(罗小元) . Chin. Phys. B, 2011, 20(4): 048901.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Metrics
Related Articles

Generation of optimal persistent formations for heterogeneous multi-agent systems with a leader constraint

Cite this article:

Online attention